10kV distribution line forest fire fault early warning method based on zero sequence voltage signal

Abstract

The invention discloses a 10kV distribution line forest fire fault early warning method based on a zero-sequence voltage signal. The method comprises the steps of obtaining zero-sequence voltage data from a 10kV power grid zero-sequence voltage transformer and performing empirical mode decomposition to obtain each intrinsic mode function; calculating Hi lbert transformation to obtain time-frequency distribution data of each modal, and calculating an instantaneous frequency, an instantaneous amplitude and an energy spectrogram according to the time-frequency distribution data; performing time-frequency analysis through the instantaneous frequency to obtain a time-varying characteristic of fluctuation when the zero-sequence voltage has a fault, and extracting main frequency components according to the time-varying characteristic of the zero-sequence voltage; fault characteristic parameters are constructed through main frequency, instantaneous amplitude and an energy spectrogram in zero sequence voltage time-varying characteristics, a threshold value is set according to the characteristic parameters, a characteristic signal during abnormal operation of the system is compared with the threshold value, and if a system fault signal exceeds the judgment threshold value, an alarm is given out, and a fault line is cut off; according to the invention, the risk of the 10kV line forest fire fault on the normal operation of the system can be reduced.

Description

Technical Field

[0001] The invention relates to the technical field of 10kV line wildfire early warning tripping, in particular to a 10kV distribution line wildfire fault early warning method based on a zero-sequence voltage signal. Background Art

[0002] Due to the imbalanced distribution of power generation energy and power load centers in my country, large-capacity, long-distance power transmission is required. Transmission and distribution lines often cross dense forests or agricultural crop areas where wildfires frequently occur, which seriously affects the safe and stable operation of the power network. When a wildfire occurs under a 10kV power line, the insulation strength of the air gap under the transmission line will be significantly reduced due to factors such as the high temperature, high conductivity, and ash content of the vegetation flame. When the flame bridges the wire, it is very likely to cause the wire to break down to the ground. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a 10kV distribution line wildfire fault warning method based on zero-sequence voltage signal. The present invention can be used to identify faults when a wildfire occurs under the 10kV line conductor. The zero-sequence voltage on the grid side is processed by empirical mode decomposition and Hilbert transform to achieve extraction through characteristic components in the time domain, and finally achieve early warning and tripping before the conductor breaks down to the ground.

[0004] To achieve the above object, the technical solution adopted by the present invention is: a 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal, comprising the following steps:

[0005] Step 1, obtaining zero-sequence voltage data from a running 10 kV power grid zero-sequence voltage transformer;

[0006] Step 2, perform empirical mode decomposition on the zero-sequence voltage to obtain each intrinsic mode function;

[0007] Step 3, calculating the Hi lbert transform of each intrinsic mode function to obtain the time-frequency distribution data of each mode, and calculating the instantaneous frequency, instantaneous amplitude, and energy spectrum according to the time-frequency distribution data;

[0008] Step 4: Perform time-frequency analysis on the instantaneous frequency to obtain the time-varying characteristics of the zero-sequence voltage fluctuation during a fault, and extract the main frequency components according to the time-varying characteristics of the zero-sequence voltage.

[0009] Step 5: Construct fault characteristic parameters through the main frequency, instantaneous amplitude, and energy spectrum in the zero-sequence voltage time-varying characteristics, and set the threshold according to the characteristic parameters. By comparing the characteristic signal when the system is operating abnormally with the threshold, if the system fault signal exceeds the judgment threshold, an alarm is issued and the faulty line is cut off.

[0010] As a further improvement of the present invention, the step 2 specifically comprises the following steps:

[0011] Step 2.1, extract local features from the original signal and find its local maximum and local minimum;

[0012] Step 2.2: Use spline interpolation to connect all the maximum and minimum points to form the upper envelope e max (t), lower envelope e min (t);

[0013] Step 2.3, calculate the average value of the upper and lower envelopes to obtain the mean curve:

[0014] Step 2.4, subtract the mean curve from the original signal to obtain the preliminary intrinsic mode function IMF candidate: h(t) = x(t) - m(t);

[0015] Step 2.5: Check whether h(t) satisfies the IMF restriction condition of the intrinsic modal component. If not, repeat steps 2.1 to 2.4 until the IMF condition is satisfied to obtain IMF. ci(t) ;

[0016] Step 2.6: Subtract IMF from the original signal ci(t) We get: r(t) = x(t) - c1(t), repeat steps 2.1 to 2.5 to get IMF c2 , until the residual signal r(t) is monotonic or no IFM can be extracted.

[0017] As a further improvement of the present invention, in step 2.5, the intrinsic modal component IMF restriction condition is specifically as follows:

[0018] (1) In the entire zero-sequence voltage data set, the number of extreme points and the number of zero points must be equal or differ by at most one; that is:

[0019] (Nz-1)≤Ne≤(Nz+1)

[0020] (2) At any point, the average value of the envelope formed by local maxima and minima must be zero; that is:

[0021]

[0022] As a further improvement of the present invention, the step 3 specifically comprises the following steps:

[0023] Step 3.1: For each IMF ci(t) Perform Hilbert transform to obtain the real and imaginary parts of the analytical signal. The calculation formula is as follows:

[0024]

[0025] In the formula, The analytical signal consists of IMF and its Hilbert transform. H[x(t)] is the Hilbert transform of IMF, where P and V are principal value integrals used to handle singular points in the integral.

[0026] Step 3.2: Calculate the instantaneous amplitude A i (t) and instantaneous phase φ i (t), the calculation formula is as follows:

[0027]

[0028] Step 3.3, calculate the instantaneous frequency f i (t), the calculation formula is as follows:

[0029]

[0030] In the formula, is the derivative of the instantaneous phase with respect to time;

[0031] Step 3.4: Calculate the energy spectrum E i (t), the calculation formula is as follows:

[0032] E i (t) = A i (t) 2 .

[0033] As a further improvement of the present invention, the step 4 is specifically as follows:

[0034] A time-frequency analysis is performed on each effective IMF instantaneous frequency obtained after Hilbert transform, and a two-dimensional time-frequency image and a Hilbert energy spectrum are drawn by MATLAB to obtain the time-varying characteristics of the zero-sequence voltage fluctuation when a fault occurs, and the main low-frequency components are extracted based on the time-varying characteristics of the zero-sequence voltage.

[0035] As a further improvement of the present invention, the step 5 is specifically as follows:

[0036] According to the main frequency components, Hilbert energy spectrum E i (t), through the Hilbert spectrum amplitude distribution characteristics and Hilbert energy spectrum IMF index, the main frequency component and Hilbert energy spectrum when the system is operating normally without wildfire disaster are compared; due to the imbalance of the system's own factors, the zero-sequence voltage change peak is set as the discrimination threshold, and the main frequency component and Hilbert energy spectrum E i (t), the Hilbert spectrum is compared with the threshold. If the main frequency component IMF and the Hilbert energy map E i(t) and the Hilbert spectrum are both out of limit, it is determined whether a wildfire disaster has occurred and whether the faulty line needs to be removed in time.

[0037] When a flame bridges the transmission line, the leakage current will increase, but the leakage current parameters cannot be obtained in the actual operating system. Only the three-phase voltage, three-phase current, and zero-sequence component can be obtained from the power grid side. Since the zero-sequence current value change is very weak when the flame causes the discharge, the present invention adopts the method of analyzing the change of zero-sequence voltage to determine whether a wildfire occurs under the wire or whether a wildfire occurs near the wire and spreads to the bottom of the wire, thereby causing a trip. By performing empirical mode decomposition and Hilbert transform on the zero-sequence voltage component, empirical mode decomposition can overcome the problem of non-adaptive basis function, without the need for pre-analysis and processing of the signal, and is convenient for application in real-time monitoring of power systems. By performing Hilbert transform on each intrinsic mode function after empirical mode decomposition, meaningful instantaneous frequency can be extracted, avoiding the disadvantage of meaningless instantaneous frequency in traditional methods. By analyzing the fluctuation change of the instantaneous frequency of the zero-sequence voltage, the function of distinguishing and warning can be achieved.

[0038] The beneficial effects of the present invention are:

[0039] The present invention takes into account that the transmission line passes through forest areas, cultivated land and other areas where wildfires frequently occur. When a wildfire occurs below the line and the flame bridges the wire, the insulation of the air gap below the wire will be greatly reduced, which may easily cause the wire to directly break down to the ground. If an early warning can be given based on the electrical characteristic quantity when a wildfire occurs, and the fire department and forestry department can be linked in time to isolate the high-risk areas near the line. Since the success rate of line tripping and reclosing caused by wildfire disasters is low, it is necessary to consider whether to shut down the line for risk avoidance based on the electrical characteristic quantity of the line under the wildfire disaster. Based on the electrical characteristic components under wildfire faults, the leakage current is the most intuitive electrical quantity reflecting the air insulation characteristics, but the leakage current cannot be detected when a wildfire occurs, so the fault characteristics of the zero-sequence component are extracted. The zero-sequence voltage component contains power frequency component, low frequency component, frequency multiplication component, and high frequency component. The empirical mode decomposition is the intrinsic mode function obtained by subtracting the upper and lower envelopes of the signal in the time domain. The result is to decompose the fluctuations or trends of different scales that actually exist in the signal step by step, from high frequency to low frequency. The empirical mode decomposition is no longer to decompose the signal into a series of sine waves, and its frequency definition is based on the instantaneous frequency. The Hilbert-Huang transform decomposes according to the change of the signal envelope. Its time resolution is constant and accurate, and the frequency resolution can be adjusted adaptively. It is suitable for processing fault signals that fluctuate over time. The instantaneous frequency and instantaneous amplitude of the IMF component are used to extract fault features, coupled with the Hilbert energy spectrum and the Hilbert spectrum, and thresholds are set for comparison and judgment. Finally, the operation management personnel will judge whether to shut down the system for risk avoidance based on the comparison results. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of an embodiment of the present invention;

[0041] Figure 2 It is a waveform diagram of the original zero-sequence voltage waveform and the IMFs superimposed waveform when no fault occurs in an embodiment of the present invention;

[0042] Figure 3 It is a waveform diagram of each inherent modal component of the zero-sequence voltage in normal operation according to an embodiment of the present invention;

[0043] Figure 4 Hilbert energy spectrum and Hilbert spectrum diagram for normal operation in an embodiment of the present invention;

[0044] Figure 5 It is a waveform diagram of the original zero-sequence voltage waveform and the IMFs superimposed waveform under a wildfire fault in an embodiment of the present invention;

[0045] Figure 6 It is a waveform diagram of each inherent modal component of zero-sequence voltage under a wildfire fault in an embodiment of the present invention;

[0046] Figure 7 Graphs showing the Hilbert energy spectrum and the Hilbert spectrum under a wildfire fault in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] Example

[0049] Figure 1 As shown, a 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal includes the following steps:

[0050] Step 1: Obtain zero-sequence voltage data from the operating 10kV power grid zero-sequence voltage transformer. When a wildfire fault occurs, the zero-sequence voltage will fluctuate, such as Figure 5 As shown;

[0051] Step 2: Perform empirical mode decomposition (EMD) on the zero-sequence voltage to obtain each intrinsic mode function IMF, such as Figure 6 As shown, the steps to obtain each intrinsic mode function IMF are as follows:

[0052] a. Extract local features from the original signal and find its local maximum and local minimum;

[0053] b. Use spline interpolation to connect all the maximum and minimum points to form the upper e max (t), lower envelope e min (t);

[0054] c. Calculate the average value of the upper and lower envelopes to obtain the mean curve,

[0055] d. Subtract the mean curve from the original signal to obtain the preliminary intrinsic mode function IMF candidate: h(t) = x(t) - m(t);

[0056] e. Check whether h(t) satisfies the IMF condition. If not, repeat ad until it satisfies the IMF condition and obtain IMF ci(t) ;

[0057] f. Subtract IMF from the original signal ci(t) We get: r(t) = x(t) - c1(t), repeat ae to get IMF c2 , until the residual signal r(t) is monotonic or no IFM can be extracted.

[0058] The intrinsic mode component IMF satisfies two constraints:

[0059] (1) In the entire zero-sequence voltage data set, the number of extreme points and the number of zero points must be equal or differ by at most one; that is:

[0060] (Nz-1)≤Ne≤(Nz+1)

[0061] (2) At any point, the average value of the envelope formed by local maxima and minima must be zero; that is:

[0062]

[0063] Step 3: For each IMF ci(t) The Hilbert transform (HHT) is calculated to obtain the time-frequency distribution data of each mode, and the instantaneous frequency, instantaneous amplitude and energy spectrum are calculated based on the time-frequency distribution data, as follows:

[0064] a. For each IMF ci(t) Perform Hilbert transform to obtain the real and imaginary parts of the analytical signal. The calculation formula is as follows:

[0065]

[0066] In the formula, The analytical signal consists of IMF and its Hilbert transform. H[x(t)] is the Hilbert transform of IMF, where P and V are principal value integrals used to deal with singular points in the integral.

[0067] b. Calculate the instantaneous amplitude A i (t) and instantaneous phase φ i (t), the calculation formula is as follows:

[0068]

[0069] c. Calculate the instantaneous frequency f i (t), the calculation formula is as follows:

[0070]

[0071] In the formula, is the time derivative of the instantaneous phase.

[0072] d. Calculate the energy spectrum E i (t), the calculation formula is as follows:

[0073] E i (t) = A i (t) 2

[0074] Step 4: Perform time-frequency analysis on each effective IMF instantaneous frequency obtained after Hilbert transform in step 3, and draw a two-dimensional time-frequency image and Hilbert energy spectrum through Matlab, such as Figure 7 As shown, the time-varying characteristics of zero-sequence voltage fluctuations when a fault occurs are obtained, and the main low-frequency components are extracted according to the time-varying characteristics of zero-sequence voltage;

[0075] Step 5: Based on the main frequency components and Hilbert energy spectrum E in step 4 i (t), through the Hilbert spectrum amplitude distribution characteristics and the Hilbert energy spectrum IMF index, and the system is operating normally without wildfire disasters (such as Figure 2 , Figure 3 The main frequency component (shown as Figure 4 As shown in the figure, the peak value of zero-sequence voltage change is set as the discrimination threshold due to the imbalance of the system itself. The main frequency component and Hilbert energy diagram E i (t), the Hilbert spectrum is compared with the threshold. If the main frequency component IMF and the Hilbert energy map E i (t) and the Hilbert spectrum are both out of limit, it is judged that a wildfire disaster has occurred and the faulty line needs to be removed in time.

[0076] In this embodiment, the zero-sequence voltage signal that is easy to collect on the grid side is first analyzed, and the average value of the upper and lower envelopes is subtracted from the zero-sequence voltage signal to obtain the intrinsic mode function. It is judged whether the obtained intrinsic mode function meets the IMF condition. If it meets the condition, it is marked as an IMF component. The extracted IMF component is subtracted from the voltage signal to obtain a residual signal, and it is checked whether the residual signal is monotonic or a constant. If it meets the condition, each intrinsic mode function will be arranged in order from high to low frequency, and then each intrinsic mode function is subjected to Hilbert transformation to solve the instantaneous frequency, instantaneous phase, instantaneous amplitude, and Hilbert spectrum. Each effective IMF instantaneous frequency obtained after Hilbert transformation is subjected to time-frequency analysis, and a two-dimensional time-frequency image and a Hilbert energy spectrum are plotted by MATLAB to obtain the time-varying characteristics of the zero-sequence voltage fluctuation when a fault occurs, and the main low-frequency components are extracted according to the time-varying characteristics of the zero-sequence voltage. According to the main frequency components, the amplitude distribution characteristics of the Hilbert spectrum and the IMF index of the Hilbert energy spectrum, they are compared with the main frequency components and the Hilbert energy spectrum when the system is operating normally without a wildfire disaster. Due to the imbalance of the system itself, the peak value of the zero-sequence voltage change is set as the discrimination threshold, and the main frequency component, Hilbert energy diagram E i (t), the Hilbert spectrum is compared with the threshold to determine whether a wildfire disaster has occurred and whether the faulty line needs to be removed in time, so as to reduce the risk of line damage caused by wildfire disasters during power grid operation.

[0077] This embodiment has the following advantages: In actual situations, when a wildfire occurs under the conductor, there is insufficient warning of the wildfire in sparsely populated areas, but the zero-sequence voltage component, as one of the electrical signals fixedly collected on the power grid side, has the advantage of being easy to obtain and analyze. According to the definition of sinusoidal frequency, when a fault occurs in the power system, its frequency characteristics are almost unrecognizable under the influence of waveform distortion, so the commonly used Fourier decomposition and wavelet transform to extract high-frequency components cannot reflect the fault characteristics, and the wavelet transform is too dependent on the selection of the wavelet basis. HHT is based on the instantaneous frequency definition, and decomposes the high-frequency components according to the signal envelope. The change in the zero-sequence voltage amplitude caused by the fault will cause the signal envelope to change, so that the signal envelope contains fault information, and there is no need to select a basis function. Its time resolution is unchanged and has high precision, and the frequency resolution can be adaptively adjusted. Since EMD decomposition no longer decomposes the signal into a series of sinusoidal waves, its frequency definition is based on the instantaneous frequency, so it has outstanding advantages in fault situations where the fault can cause the signal envelope to change but the sinusoidal frequency changes slightly. By comparing the decomposed main frequency components and the Hilbert energy spectrum with the established threshold, a basis for judgment is provided when power companies determine whether wildfire failures require shutdown to avoid risks.

[0078] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal, characterized in that: The following steps are involved: Step 1, obtaining zero-sequence voltage data from a running 10 kV power grid zero-sequence voltage transformer; Step 2, perform empirical mode decomposition on the zero-sequence voltage to obtain each intrinsic mode function; Step 3, calculating the Hilbert transform of each intrinsic mode function to obtain the time-frequency distribution data of each mode, and calculating the instantaneous frequency, instantaneous amplitude, and energy spectrum according to the time-frequency distribution data; Step 4: Perform time-frequency analysis on the instantaneous frequency to obtain the time-varying characteristics of the zero-sequence voltage fluctuation during a fault, and extract the main frequency components according to the time-varying characteristics of the zero-sequence voltage. Step 5: Construct fault characteristic parameters through the main frequency, instantaneous amplitude, and energy spectrum in the zero-sequence voltage time-varying characteristics, and set the threshold according to the characteristic parameters. By comparing the characteristic signal when the system is operating abnormally with the threshold, if the system fault signal exceeds the judgment threshold, an alarm is issued and the faulty line is cut off.

2. The 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal according to claim 1 is characterized in that: The step 2 specifically includes the following steps: Step 2.1, extract local features from the original signal and find its local maximum and local minimum; Step 2.2: Use spline interpolation to connect all the maximum and minimum points to form the upper envelope e max (t), lower envelope e min (t); Step 2.3, calculate the average value of the upper and lower envelopes to obtain the mean curve: Step 2.4, subtract the mean curve from the original signal to obtain the preliminary intrinsic mode function IMF candidate: h(t) = x(t) - m(t); Step 2.5: Check whether h(t) satisfies the IMF restriction condition of the intrinsic modal component. If not, repeat steps 2.1 to 2.4 until the IMF condition is satisfied to obtain IMF. ci(t) ; Step 2.6: Subtract IMF from the original signal ci(t) We get: r(t) = x(t) - c1(t), repeat steps 2.1 to 2.5 to get IMF c2 , until the residual signal r(t) is monotonic or no IFM can be extracted.

3. The 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal according to claim 2 is characterized in that: In step 2.5, the intrinsic modal component IMF restriction conditions are as follows: (1) In the entire zero-sequence voltage data set, the number of extreme points and the number of zero points must be equal or differ by at most one; that is: (Nz-1)≤Ne≤(Nz+1) (2) At any point, the average value of the envelope formed by local maxima and minima must be zero; that is:

4. The 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal according to claim 2 or 3 is characterized in that: The step 3 specifically includes the following steps: Step 3.1: For each IMF ci(t) Perform Hilbert transform to obtain the real and imaginary parts of the analytical signal. The calculation formula is as follows: In the formula, The analytical signal consists of IMF and its Hilbert transform. H[x(t)] is the Hilbert transform of IMF, where P and V are principal value integrals used to handle singular points in the integral. Step 3.2: Calculate the instantaneous amplitude A i (t) and instantaneous phase φ i (t), the calculation formula is as follows: Step 3.3, calculate the instantaneous frequency f i (t), the calculation formula is as follows: In the formula, is the derivative of the instantaneous phase with respect to time; Step 3.4: Calculate the energy spectrum E i (t), the calculation formula is as follows: E i (t)=A i (t) 2 。 5. The 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal according to claim 4 is characterized in that: The step 4 is specifically as follows: A time-frequency analysis is performed on each effective IMF instantaneous frequency obtained after Hilbert transform, and a two-dimensional time-frequency image and a Hilbert energy spectrum are drawn by MATLAB to obtain the time-varying characteristics of the zero-sequence voltage fluctuation when a fault occurs, and the main low-frequency components are extracted based on the time-varying characteristics of the zero-sequence voltage.

6. The 10kV distribution line mountain fire fault early warning method based on zero-sequence voltage signal according to claim 5 is characterized in that: The step 5 is specifically as follows: According to the main frequency components, Hilbert energy spectrum E i (t), through the Hilbert spectrum amplitude distribution characteristics and Hilbert energy spectrum IMF index, the main frequency component and Hilbert energy spectrum when the system is operating normally without wildfire disaster are compared; due to the imbalance of the system's own factors, the zero-sequence voltage change peak is set as the discrimination threshold, and the main frequency component and Hilbert energy spectrum E i (t), the Hilbert spectrum is compared with the threshold. If the main frequency component IMF and the Hilbert energy map E i (t) and the Hilbert spectrum are both out of limit, it is determined whether a wildfire disaster has occurred and whether the faulty line needs to be removed in time.

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